Urban Air Mobility (UAM) systems offer a three-dimensional transportation alternative by using low-altitude airspace, with the potential to reduce travel times and improve access to mobility in regions underserved by current transportation systems. To support efficient design and operation of UAM systems, we develop an integrated optimization framework in response to three interrelated challenges: (i) land use, aeronautical feasibility, community acceptance and other factors that restrict the number of potential locations for vertiports, (ii) bidirectional demand–supply interaction that needs to be considered, as the level of service influences demand for UAM and operators adjust the level of service in response to demand, and (iii) strong interactions between strategic decisions on the distribution of ground infrastructure, tactical decisions on eVTOL fleet size and operational decisions on dispatching and repositioning. Analyzing the decisions in isolation can lead to poor estimates of the overall system performance. The framework consists of (1) a knock-off criteria analysis model for the identification of a realistic set of candidate locations for vertiports, (2) integer programming models in which strategic, tactical and operational decision levels are modeled, and (3) pre-processing techniques to generate near-optimal solutions for real-world instances. By applying the framework in a large-scale real-world setting in the Île-de-France region, we demonstrate complex interactions between strategic, tactical, and operational decision levels and customer demand, revealing various trade-offs between operator profit and traveler generalized travel costs.

Punctuality is a key performance indicator for any airline, especially hub-and-spoke airlines, given their focus on short passenger connections. Flights that are delayed at departure need to compensate for lost time whilst airborne. Because fuelling takes place well before scheduled departure, predicted departure delays determine the planned fuel amounts for en-route speed optimization. To prevent unnecessary fuel burn, airlines benefit from highly accurate departure delay predictions. This study aims to extend previous work on airline departure delay forecasting to a dynamic and probabilistic domain, whilst incorporating novel day-of-operations airline information to further minimize prediction errors. Random Forest, CatBoost, and Deep Neural Network models are proposed for a case study on departure flights of a major hub-and-spoke airline from its hub airport between 1 January 2020 and 1 August 2023. The Random Forest model is selected for its probabilistic performance and high accuracy in predicting delays between 5 and 25 min, for which en-route speed optimization has the largest effect. At the 90 min prediction horizon, the model reaches a Mean Absolute Error of 8.46 min and a Root Mean Square Error of 11.91 min. For 76% of flights, the actual delay is within the predicted probability distribution range. Finally, this study puts a strong emphasis on explainability. Flight dispatchers are therefore provided with the main factors impacting the prediction, explaining the context of the flight. The versatility of the model is demonstrated in two shadow runs within the procedures of an international airline, where delays caused by familiar and unfamiliar factors were successfully predicted.

Reducing uncertainty in air traffic flow management is crucial for maintaining safety and efficiency in modern aviation. In particular, forecasting Actual Take-Off Times (ATOT) for flights across Europe is challenging due to the diverse flight-specific variables and operational conditions. Additionally, to help operations, this prediction must be done well in advance in order to prevent future traffic densities from being higher than the airspace capacity. However, recent studies often make predictions on shorter horizons and do not consider the effect of knock-on delays. This study covers this gap, by focusing on larger prediction horizons and different types of delay. We enhance ATOT prediction for flights arriving at Amsterdam Schiphol Airport from European out-stations by leveraging machine learning techniques, specifically a Long Short-Term Memory (LSTM) neural network, augmented with a Multihead Attention mechanism. A model capable of capturing complex temporal dependencies and operational factors influencing the ATOT is developed utilizing data from Electronic Flight Data messages, weather reports and a EUROCONTROL dataset. The model’s performance is evaluated against traditional ensemble methods and the current Decision Support Tool (DST) system used by Luchtverkeersleiding Nederland (LVNL). Results indicate that the LSTM model outperforms existing models including a reproduction of the DST, achieving a Mean Absolute Error of 12.05 minutes at a forecast horizon of 4 hours, demonstrating significant improvements. Finally, this assessment underscores the importance of factors such as the knock-on effect in delay prediction can significantly enhance demand forecasting, leading to more efficient air traffic management and reduced delays.

Aircraft carry additional fuel reserves, referred to as contingency fuel, used to account for unforeseen events during a flight. Previous research has attempted to quantify the magnitude of such events, most notably the probability of adverse weather or ATFM regulation, yet their inherent unpredictability introduces uncertainty and frequently results in the overestimation of contingency fuel requirements. Recent studies use data-driven fuel-burn predictions to better estimate contingency fuel sizing; however, most are confined to specific routes or regions, limiting generalizability. To address this, we utilise real operational airline data covering both regional and intercontinental flights, and develop a quantile regression framework for predicting contingency fuel requirements, capable of adapting to more diverse set of flight characteristics. Our framework integrates flight-plan data, TAF weather forecasts, and proxy congestion features to predict required contingency fuel at varying quantile levels, enabling trade-offs between efficiency and safety. Unlike the current Statistical Contingency Fuel process, which applies different coverage levels by risk category, this evaluation uses a single fixed quantile for all flights when generating predictions. In a four-month out-of-sample evaluation, a single fixed quantile matched the safety performance of the Statistical Contingency Fuel process while reducing excess fuel carriage by up to 235,364 kg (≈11%). A more conservative quantile configuration yielded smaller savings but reduced abnormal flight-phase events by 22.2%. The key drivers of the final predictions are evaluated, offering pilots and dispatchers transparent explanations that can build trust and reduce reliance on discretionary fuel loading.

A-check maintenance scheduling is a complex and critical undertaking for airlines requiring an efficient allocation of resources. Current state-of-the-art focuses primarily on long-term A-check planning, typically targeting a longer scheduling horizon while foregoing individual task planning. This paper introduces a novel integrated approach for A-check scheduling at a seasonal level for an airline fleet, which accounts for both repetitive and one-off maintenance tasks. The A-check maintenance scheduling problem is formulated as a mixed-integer linear program (MILP), which optimises for minimal interval waste and timely initiation of one-off activities. Furthermore, we explore the scalability and flexibility of this problem by proposing three distinct distributed architectures. Subsets of maintenance tasks are scheduled by individual components, guided by a genetic algorithm (GA) acting as a global optimiser, with each architecture managing shared resources differently. We demonstrate our method with a case study from a major European airline using recent data of a fleet of wide-bodied passenger aircraft. While our MILP baseline produces comparable results to real-world schedules within minutes, the distributed architectures, despite their potential for scalability, generally underperform compared to the central planner. We analyse the degradation of solution quality across these distributed architectures, providing insights into their design limitations and the inherent indivisibility of the problem. We propose that our central MILP-based scheduler can be used by airlines as a decision-support tool for A-check task planning at the seasonal level.

Reactionary delays are a critical challenge in airline operations, especially within hub-spoke networks, where disruptions at spoke airports propagate and amplify throughout the fleet. Accurate prediction of these delays is essential for effective network planning, as errors can lead to flight cancellations, missed connections, and curfew infringements. However, current state-of-the-art delay prediction models do not fully integrate all elements that cause reactionary delays and affect subsequent operations. This study aims to close this gap by using a Graph Attention Network (GAT) model to predict reactionary delay distributions within a fleet network and identify the most critical flights through the analysis of attention weights. Using operational data from Swiss International Air Lines’ short-haul fleet, the GAT model integrates node-level features, such as flight-specific parameters, and edge-level features, including rotational dependencies and passenger connections, to capture the spatial-temporal dynamics of delay propagation. The GAT model achieved reliable predictive accuracy, particularly on medium-delay days, of a root mean squared error of 15.59 minutes and a mean absolute error of 10.50 minutes. The results further reveal that the model comprehends the ripple effects caused by rotation delays. Furthermore, its attention weights confirm its capability to identify critical flights and connections, enabling the airline to allocate resources more effectively.

The complexity of airline operations requires operations planning to be divided into multiple problems solved sequentially by the respective departments. This is particularly the case for (1) network planning and (2) maintenance planning. Despite the close interaction of these two departments, airlines typically evaluate plans from both domains separately. However, an integrated perspective is necessary to develop robust plans and effective recovery policies in this intrinsically uncertain environment. This paper presents a new modular, stochastic, discrete event simulation model of airline operations named ANEMOS (Airline Network and Maintenance Operations Simulation). ANEMOS contains both network and maintenance dynamics, allowing the evaluation of plans, policies, and scenarios from both domains. The model is validated using data from a major European airline. We show that the simulated results closely resemble the airline's historical operational performance. ANEMOS is tested with a use-case investigating the effects of adding a second reserve aircraft to a fleet of fifty wide-body aircraft. The results show that the second reserve is capable of reducing cancellations by 55%. However, such does not cover the lost revenue associated with keeping an aircraft non-operational for a part of the time.